Context-Based Automatic Local Image Enhancement

Sung Ju Hwang¹, Ashish Kapoor², and Sing Bing Kang²

¹The University of Texas, Austin, TX, USA
sjhwang@cs.utexas.edu

²Microsoft Research, Redmond, WA, USA
akapoor@microsoft.com
sbkang@microsoft.com

Abstract. In this paper, we describe a technique to automatically enhance the perceptual quality of an image. Unlike previous techniques, where global statistics of the image are used to determine enhancement operation, our method is local and relies on local scene descriptors and context in addition to high-level image statistics. We cast the problem of image enhancement as searching for the best transformation for each pixel in the given image and then discovering the enhanced image using a formulation based on Gaussian Random Fields. The search is done in a coarse-to-fine manner, namely by finding the best candidate images, followed by pixels. Our experiments indicate that such context-based local enhancement is better than global enhancement schemes. A user study using Mechanical Turk shows that the subjects prefer contextual and local enhancements over the ones provided by existing schemes.

LNCS 7572, p. 569 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012